docstr: :param x: the input into the bijection
  :type x: torch.Tensor
  Invokes the bijection x => y; in the prototypical context of a
  :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from
  the base distribution (or the output of a previous transform)
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code: def _call(self, x):
      """
      :param x: the input into the bijection
      :type x: torch.Tensor
      Invokes the bijection x => y; in the prototypical context of a
      :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from
      the base distribution (or the output of a previous transform)
      """
  
      M = self.weights() if callable(self.weights) else self.weights
      return self._exp(x, M)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 4 for calling the ConditionedMatrixExponential obj's underlying member method code with example usage: obj._call(x) and returns: self